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    <title>浏阳德塔软件开发有限公司 女娲计划</title>
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    <br/>第一章_德塔自然语言图灵系统
    <br/> 作者: 罗瑶光, Author:Yaoguang.Luo<br/>
    <br/> 基础应用: 元基催化与肽计算 编译机的语言分析机
    <br/>
    <br/>
    <br/> 测试速度: 单机联想Y7000笔记本win10 实测峰值每秒 中文分词1630~1650万+中文字,
    词库65000+, 函数准确率100%, 缺失语法函数 0. 2%-, 算法准确率 99. 8%+, 100%完整开放源码,
    在api与书籍中.

    <br/> 测试效果: 输入:
    如果从容易开始于是从容不迫天下等于是非常识时务必为俊杰沿海南方向逃跑他说的确实在理结婚的和尚未结婚的提高
    产品质量中外科学名著内科学是临床医学的基础内科学作为临床医学的基础学科重点论述人体各个系统各种疾病
    的病因发病机制临床表现诊断治疗与预防.

    <br/> 输出结果:
    如果+从+容易+开始+于是+从容不迫+天下+等于+是非+常识+时务+必+为+俊杰+沿海+南+方向+逃跑+他+说+的+确实+
    在理+结婚+的+和+尚未+结婚+的+提高+产品质量+中外+科学+名著+内科学+是+临床+医学+的+基础+内科学+作为
    +临床+医学+的+基础+学科+重点+论述+人体+各个+系+统+各种+疾病+的+病因+发病+机制+临床+表现+诊断+
    治疗+与+预防+++++
    <img class="banner_img" style="width: 100%" src="../images/5_7108/1/1_1.jpg"
         alt="浏阳德塔软件开发有限公司,罗瑶光"/>

    <br/> 定义: 德塔分词是一种 基于神经网络索引字典进行文章文字关联切割, 然后进行前序遍历其词性组合匹配
    , 按文学语法定义搭配 的规则切词引擎. 德塔分词的催化切词优化方式主要包含:<br/>

    <br/>
    1 索引字典进行细化拆分加速. 细化微分能够有效的减少内存运算体积, 减少资源占用. 从而提高当前的
    关于堆栈的搜索和操作速度.
    2 函数进行使用频率统计排列加速优化. 函数的使用频率统计排列一旦有高频提前的操作,
    那么具备了队列优先意识,
    可进行代谢.
    3 动态类卷积遍历内核的关键字优化. 动态卷积内核的总数直接关联到计算复杂度, 计算越复杂,
    成本便越高,
    时间开销也越大, 当然自适应精度也相应提高.
    4 函数文件和 函数文件名 进行新陈代谢, 二次新陈代谢优化
    索引编码加速. 函数越细化, 逻辑便越简洁, 那么单位的call计算便越均匀, 这种balanced操作越有条理.
    5 文学切词语法函数的细化优化加速. 文学切词问题便更有针对性. 定义者
    罗瑶光
    <br/> Definition: Deta parser word segmentation was a word cutting
    engine were based on the index forest dictionary of the neural network
    map, which carried out the word associational cutting, and then
    circulated the traversal function of the part of speech (POS) and
    combination-matching, and defined the collocation according to the
    Chinese literary grammar.
    <br/>
    <br/> 1 The index dictionary could accelerate the refinement and
    splitting. The refinement differentiation could effectively reduce the
    memory operation volume and the occupation of resources, so as to
    improve the current stack search and operational speed. 2 The function
    could accelerate the optimization of the frequency’s usage and
    statistical arrangement. Once the function had the high-frequenct
    advance operation, means It had a queue-priority consciousness and
    could be metabolized here. For example, the higher frequent logic
    sections could be arranged at the top by using Sequences of Von-Neumann
    (from top to bottom, from left to right). 3 The total number of dynamic
    convolutional kernels was directly related to the computational
    complexity. The more complex the computation was, the higher the cost
    would be. Of course, the higher the time cost would be, the adaptive
    accuracy would also be improved accordingly. 4 Functional prototypes
    and function-file names were metabolized by PDE, and the secondary
    metabolism was optimized by Initons to accelerate index encoding. The
    more detailed the function, the more concise logic, the more uniform
    the unit-called calculation, and the more organized the balanced
    operation. 5 The refinement and optimization of literary lexical
    functions were accelerated. The literary lexical problem was more
    targeted.
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